AIMC Topic: Speech Perception

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Restoring speech intelligibility for hearing aid users with deep learning.

Scientific reports
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep l...

Multimodal Sensor-Input Architecture with Deep Learning for Audio-Visual Speech Recognition in Wild.

Sensors (Basel, Switzerland)
This paper investigates multimodal sensor architectures with deep learning for audio-visual speech recognition, focusing on in-the-wild scenarios. The term "in the wild" is used to describe AVSR for unconstrained natural-language audio streams and vi...

Detecting Lombard Speech Using Deep Learning Approach.

Sensors (Basel, Switzerland)
Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the ...

Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement.

ACS applied materials & interfaces
Silent communication based on biosignals from facial muscle requires accurate detection of its directional movement and thus optimally positioning minimum numbers of sensors for higher accuracy of speech recognition with a minimal person-to-person va...

Recognition of the Effect of Vocal Exercises by Fuzzy Triangular Naive Bayes, a Machine Learning Classifier: A Preliminary Analysis.

Journal of voice : official journal of the Voice Foundation
OBJECTIVES: Machine learning (ML) methods allow the development of expert systems for pattern recognition and predictive analysis of intervention outcomes. It has been used in Voice Sciences, mainly to discriminate between healthy and dysphonic voice...

Design of Political Online Teaching Based on Artificial Speech Recognition and Deep Learning.

Computational intelligence and neuroscience
With the emergence of the information age, computers have entered the homes of ordinary people and have become essential daily appliances for people. The integration of people and computers has become more popular and in-depth. Based on this situatio...

Regional Language Speech Recognition from Bone-Conducted Speech Signals through Different Deep Learning Architectures.

Computational intelligence and neuroscience
Bone-conducted microphone (BCM) senses vibrations from bones in the skull during speech to electrical audio signal. When transmitting speech signals, bone-conduction microphones (BCMs) capture speech signals based on the vibrations of the speaker's s...

A hierarchy of linguistic predictions during natural language comprehension.

Proceedings of the National Academy of Sciences of the United States of America
Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However,...

Deep Learning Scoring Model in the Evaluation of Oral English Teaching.

Computational intelligence and neuroscience
This study is aimed at improving the accuracy of oral English recognition and proposing evaluation measures with better performance. This work is based on related theories such as deep learning, speech recognition, and oral English practice. As the l...

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things.

Computational intelligence and neuroscience
The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the ...